Documentation: expand sparse least squares example to other algorithms (Douglas-Rachford) about proximalalgorithms.jl HOT 7 OPEN

test_lasso.zip

from proximalalgorithms.jl.

In that example, a function can be passed as f argument to the proximal-gradient-based algorithms, since gradients will be taken with respect to f via automatic differentiation.

For other algorithms, including DouglasRachford and DRLS, the proximal mapping of f is needed. Therefore a regular Julia function will not work. Instead, you can construct an equivalent function using eg ProximalOperators. For this specific example, something along the lines of this should work, for appropriate A and b and scaling coefficient: this is used a little below exactly with DRLS.

I think this is once again a documentation issue: feel free to leave this open, I’ll just adjust the title.

from proximalalgorithms.jl.

It seems as if ProximalAlgorithms.DRLS(tol = 10 * TOL, directions=acc) doesn't care about the values of the tol parameter.

from proximalalgorithms.jl.

It seems as if ProximalAlgorithms.DRLS(tol = 10 * TOL, directions=acc) doesn't care about the values of the tol parameter.

Do you have an example?

It definitely cares about the value of tol, but like other Newton-type methods it may suddenly converge fast to the solution, and therefore appear to be insensitive to the tolerance.

from proximalalgorithms.jl.

It's a rather large data set, can I send it by email? I have tested your test_small_lasso.jl and DRLS works fine on that small data, but not on my larger data. The interesting point is that all other methods from that file works fine on my data.

from proximalalgorithms.jl.

How big is it? Otherwise you could upload the data somewhere and share the link here, in case it’s fine to have the data publicly available

from proximalalgorithms.jl.

@patwa67 looks like there is a bug in the way DRLS displays progress:

Fixing it, thanks!

from proximalalgorithms.jl.